Instructions to use abacusai/Smaug-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abacusai/Smaug-Mini with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="abacusai/Smaug-Mini")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("abacusai/Smaug-Mini")
model = AutoModelForMultimodalLM.from_pretrained("abacusai/Smaug-Mini", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abacusai/Smaug-Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abacusai/Smaug-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Smaug-Mini",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/abacusai/Smaug-Mini

SGLang

How to use abacusai/Smaug-Mini with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "abacusai/Smaug-Mini" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Smaug-Mini",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "abacusai/Smaug-Mini" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Smaug-Mini",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use abacusai/Smaug-Mini with Docker Model Runner:
```
docker model run hf.co/abacusai/Smaug-Mini
```

prann-ab commited on Sep 9

Commit

a58236f

verified ·

1 Parent(s): 87eaab5

Move notes toggle below the LiveBench section

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README.md +8 -8

README.md CHANGED Viewed

@@ -148,6 +148,14 @@ This card describes the training approach and the evaluation results. Dataset co
 <div style="font-size:11.5px;color:#6B6B6B;margin-top:6px">Bold = best score in the row. Shading is relative within each row (darker = higher). Qwen3.6-27B, Qwen3.7-Plus, and Opus4.6 Max scores are as reported on the Qwen3.8-27B model card unless noted; the Opus 4.6 AutomationBench score is from our own harness run and its JobBench score is a reported number, not our run; LiveBench rows use the public <a href="https://livebench.ai">livebench.ai</a> leaderboard (its Opus 4.6 entry runs at high effort); the Qwen3.7-Plus AutomationBench score and all MMMU-Pro comparison scores are from <a href="https://artificialanalysis.ai">Artificial Analysis</a> (its Opus 4.6 MMMU-Pro entry runs at max effort); &mdash; = not reported.</div>
 </div>
 <details>
 <summary><b>Notes</b></summary>
@@ -157,14 +165,6 @@ Base points: JobBench, IFBench, and GPQA-diamond are the scores published on the
 </details>
-### LiveBench category profile
-*Scores 0–100; overall = mean of the seven category averages. Both models' scores are the published [livebench.ai](https://livebench.ai) leaderboard entries (Smaug-Mini listed under the finetunes filter).*
-<div align="center">
-  <img src="figures/smaug_mini_livebench_categories.png" width="100%" alt="Smaug-Mini LiveBench category profile vs Qwen3.8-27B board entry">
-</div>
 ## 4. Training Approach
 Smaug-Mini was trained to make multi-turn tool use and automation episodes more reliable end to end. It is trained with on-policy reinforcement learning (GRPO) over multi-turn, tool-using automation episodes with verified, outcome-based rewards - an episode is rewarded for measurably completing its task in the environment. Training is a LoRA adapter over the language trunk only, merged into the base weights as a full delta; the vision tower and multimodal projection are bitwise-identical to the base release.

 <div style="font-size:11.5px;color:#6B6B6B;margin-top:6px">Bold = best score in the row. Shading is relative within each row (darker = higher). Qwen3.6-27B, Qwen3.7-Plus, and Opus4.6 Max scores are as reported on the Qwen3.8-27B model card unless noted; the Opus 4.6 AutomationBench score is from our own harness run and its JobBench score is a reported number, not our run; LiveBench rows use the public <a href="https://livebench.ai">livebench.ai</a> leaderboard (its Opus 4.6 entry runs at high effort); the Qwen3.7-Plus AutomationBench score and all MMMU-Pro comparison scores are from <a href="https://artificialanalysis.ai">Artificial Analysis</a> (its Opus 4.6 MMMU-Pro entry runs at max effort); &mdash; = not reported.</div>
 </div>
+### LiveBench category profile
+*Scores 0–100; overall = mean of the seven category averages. Both models' scores are the published [livebench.ai](https://livebench.ai) leaderboard entries (Smaug-Mini listed under the finetunes filter).*
+<div align="center">
+  <img src="figures/smaug_mini_livebench_categories.png" width="100%" alt="Smaug-Mini LiveBench category profile vs Qwen3.8-27B board entry">
+</div>
 <details>
 <summary><b>Notes</b></summary>
 </details>
 ## 4. Training Approach
 Smaug-Mini was trained to make multi-turn tool use and automation episodes more reliable end to end. It is trained with on-policy reinforcement learning (GRPO) over multi-turn, tool-using automation episodes with verified, outcome-based rewards - an episode is rewarded for measurably completing its task in the environment. Training is a LoRA adapter over the language trunk only, merged into the base weights as a full delta; the vision tower and multimodal projection are bitwise-identical to the base release.